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Four things to know about the Intel Pathfinder for RISC-V

EDN Network - Fri, 09/02/2022 - 10:43

As if Intel testing the RISC-V waters wasn’t news in itself, the semiconductor behemoth’s Intel Pathfinder for RISC-V initiative is now making the headlines. RISC-V is an open standard instruction set architecture (ISA) that offers chip developers the freedom to configure a custom processor with standard extensions and configuration options.

Vijay Krishnan, general manager of RISC-V Ventures at Intel, acknowledges that the adoption of RISC-V is at an inflection point across multiple markets and applications. Intel, which spearheads the x86 ISA world, has been proactive in the RISC-V space for quite some time. Now, its Pathfinder platform is promising to bolster the RISC-V design ecosystem for developing and prototyping chip designs with robust software and industry-standard toolchains.

Source: Intel

This blog summarizes the key technical aspects and IC designer takeaways of this Intel initiative.

  1. Two Pathfinder versions

Intel Pathfinder is initially available in two versions: Starter Edition and Professional Edition. Starter Edition—intended for the hobbyist, academia, and research community—is available as a free download. Professional Edition, which comes with broad ecosystem support, targets firms involved in developing commercial silicon and software.

For instance, it includes fixed platform kits (FPKs) from Imperas to offer delivery of pre-configured platform models as a binary installation without licensing management or complex installation set-up.

  1. Intel Stratix FPGAs

The initiative is built around Intel’s Stratix FPGAs and FPGA boards for developing and prototyping system-on-chips (SoCs) based on the RISC-V processor platform. FPGAs have been an essential part of the IC design value chain in the prototyping and production stages. Here, Intel FPGAs come with software tools like Intel Stratix 10 GX FPGA Development Kit, which allows chip designers to boot Linux OS or upload compute kernels to the FPGA board during the pre-silicon development.

  1. Cores and IPs

Intel Pathfinder will allow a variety of RISC-V cores and other IPs to be instantiated on FPGA and simulator platforms. And it will do so while IPs utilize prominent operating systems and toolchains within a unified integrated development environment (IDE). That, in turn, saves time in assembling and testing different IP combinations in a single design environment.

Exploring different configurations and combinations of IP in the early stages of the SoC development cycle is highly beneficial. For example, Codasip is making its 32-bit L31 core available through the Professional Edition of the Intel Pathfinder for RISC-V program.

Intel Pathfinder provides a common environment for accessing RISC-V and peripheral IP on its FPGA boards. Take the case of Andes Technology, which has ported its 512-bit vector processor core NX27V and 64-bit superscalar multicore AX45MP to the Intel Stratix 10 GX FPGA board.

  1. Software toolchains in a unified IDE

With Intel Pathfinder, the Santa Clara, California-based chipmaker is embracing a software-first development approach. That encompasses a unified IDE, software toolchains and commonly used operating systems, essential ingredients for embedded software developers. Then there are reference models for hardware verification use cases such as compliance, verification and test development as well as software development targets for firmware, drivers, OS porting, and application development.

Intel Pathfinder, for example, includes a reference model to provide the simulation environment that supports bare metal or applications with operating systems like Linux or RTOS as a starting point. Also, there is Imperas simulator with proprietary just-in-time code-morphing simulation technology. It can be integrated within other standard EDA environments such as SystemC and SystemVerilog as well as well-known simulation/emulation tools from Cadence, Siemens EDA, and Synopsys plus the cloud-based offering from Metrics Technologies.

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Accelerating the Commercialization of Stretchable Electronics

ELE Times - Fri, 09/02/2022 - 08:40

Carnegie Mellon mechanical engineering researchers have developed a new scalable and reproducible manufacturing technique that could accelerate the mainstream adoption and commercialization of soft and stretchable electronics.

The next generation of robotic technology will produce soft machines and robots that are safe and comfortable for direct physical interaction with humans and for use in fragile environments. Unlike rigid electronics, soft and stretchable electronics can be used to create wearable technologies and implantable electronics where safe physical contact with biological tissue and other delicate materials is essential.

Soft robots that safely handle delicate fruits and vegetables can improve food safety by preventing cross-contamination. Robots made from soft materials can brave the unexplored depths of the sea to collect delicate marine specimens. And the many biomedical applications for soft robots include wearable and assistive devices, prostheses, soft tools for surgery, drug delivery devices, and artificial organ function.

But creating these nearly imperceptible components that can seamlessly integrate with human life is only the first step. Mainstream adoption and commercialization of soft and stretchable electronics will require the development of new manufacturing techniques that are scalable and reproducible.

Although a variety of methods have already demonstrated the ability to fabricate liquid-metal-based devices on a smaller scale in labs, these methods have not yet resulted in the critical combination of desired features required to produce liquid-metal-based soft and stretchable electronics at a commercially viable scale.

A team of researchers from Carnegie Mellon University’s College of Engineering seeks to change this with a novel method they have developed for mass manufacturing of liquid-metal-based soft and stretchable electronic devices.

Kadri Bugra Ozutemiz, who recently earned his Ph.D. in mechanical engineering, has developed a new approach that achieves scalability, precision, and microelectronic compatibility by combining the use of liquid metal with photolithography and wafer-based dip coating.

Ozutemiz, who worked with Carmel Majidi and Burak Ozdoganlar, both professors of mechanical engineering, explains that liquid metals have become popular in recent years as a conductor for stretchable circuits to create sensors and antennas as well as soft and stretchable wiring for various electronics and robotics applications.

The gallium-based alloy, eutectic gallium–indium (EGaIn), is liquid at room temperature, can freely flow inside channels, has high electrical conductivity, and can be deformed as long as it is encapsulated in another medium.

“We had to better understand the inherent properties of gallium-based liquid alloys to overcome challenges that make them unsuitable for mass manufacturing,” said Ozutemiz.

The most significant challenge was that a thin gallium-oxide “skin” rapidly forms when the liquid metal is exposed to air, which makes it difficult to achieve a uniform and continuous shape or geometry. The liquid metal sticks everywhere, flowing into a wide variety of changeable shapes.

“Our team devised a novel approach that combines selective metal-alloy wetting that deposits the liquid metal into the desired circuit layout with a dip coating process that dissolves the oxide skin that results when EGaln is exposed to the air,” explained Ozutemiz.

Thin metal traces, made of affordable and readily available copper, are first lithographically patterned onto an elastomer surface as a wetting layer. The traces serve as templates for selectively depositing the EGaln onto the silicone rubber surface.

In order to dissolve the oxide skin while maintaining the selective deposition of the liquid metal, the researchers devised a novel approach that combined the selective metal-alloy wetting with a dip coating process.

Dip-coating, which has been used in the microelectronics industry, but not with liquid metals, facilitates the deposition of EGaIn selectively onto the circuit layout defined by lithographically patterned copper traces on elastomer-coated wafers in a scalable manner.

An automated, high-precision motion system and a two-layer dipping bath are used to deposit the EGaIn onto the patterned copper wetting layer. The bath includes a thin layer of aqueous sodium hydroxide (NaOH) solution at the top surface, followed by the EGaIn. The NaOH solution facilitates the removal of oxide skin and of any oxidation on the surface of the copper traces when the patterned wafer is dipped into the bath. The wafer is then immersed into the bath, and after a short dwell time, is withdrawn at a prescribed speed that controls the amount of liquid deposited on the substrate.

The researchers used a custom-built simple machine to dip the wafers into the bath. By controlling the withdrawal speed, they successfully produced repeatable liquid metal geometries.

In future testing, they will work to control parameters such as withdrawal speed and the amount of time the wafer remains in the bath in order to better understand what affect each variable has on the resulting geometry. But for now, they have established a viable process for the mass production of liquid metal circuits that can be used in a wide variety of soft robotic and electronics applications.

“For us, what was most important was that we achieve repeatable results with a standard process that is already used by chip manufacturers,” said Ozutemiz, who explained that by introducing a new material into a well-established process, manufacturers will be able to scale production that will allow for more widespread adoption of these innovative soft robots and electronic devices.

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Additive Manufacturing: Laser-based Multiple Metallic Material

ELE Times - Fri, 09/02/2022 - 08:39

Compared with general additive manufacturing (AM) methods, Multi-material additive manufacturing (MMAM) enables a higher level of design freedom, such as integrating materials, structure, and function to achieve tailorable functionalities (e.g., local wear resistance, high thermal conductivity, thermal insulation, and chemical corrosion resistance, etc.). However, MMAM of metallic materials is a recent research activity still in its embryonic stage. Notably, there has been so far no comprehensive review about metallic MMAM covering both macroscale fabrication to microscale fabrication.

summarized the recent progress on laser-based multi-material additive manufacturing (MMAM) technologies, including laser powder bed fusion (LPBF), laser-based directed energy deposition (L-DED) and laser-induced forward transfer (LIFT), for macro-and micro-scale fabrication of multiple metallic materials.

The use of LPBF method and L-DED method to produce large multi-material parts has become a reality because of various new inventions of the dissimilar powder materials deposition mechanisms. The potential applications of these technologies are to fabricate functionally integrated components widely used in aerospace, marine, nuclear power, and medical industries.

As for micro-AM of multiple metallic objects, solid LIFT and fluid LIFT are the technologies mainly employed currently, because their material transfer mechanism, jetting metallic droplets from one donor plate to the building substrate, is very suitable for printing dissimilar materials together. There is no contact between the donor and the printed object during material depositing, hence the dissimilar raw material cross-contamination problem is avoided. The potential applications of micro-scale metallic AM technologies include: 3D micro-scale metallic structures, energy storage components, electronic components, biomolecules, biochemical sensors and cells, and even directly transferring functionally devices to the surface of other parts.

Laser-based MMAM technologies are still at their early stage, hence many scientific and technical challenges are waiting for solutions. The research team led by Prof Lin LI, from the University of Manchester, reported the state of the art of this field and pointed out both the urgent challenges and relevant high-value future research topics.

The equipment of MMAM technologies may be significantly different from the standard single-material AM processes due to the dissimilar material dispensing challenge (i.e., how to deposit the right material at the desired region in the spatial space). This work summarizes the material delivery method, joining of dissimilar materials, processing parameters and printed MMAM components’ performance. The material delivery methods for each MMAM method are introduced and their merits are compared. Three typical dissimilar material joining methods are presented. The material composition of MMAM-printed functional gradient materials (FGMs) is constantly changing. Therefore, the optimized laser parameters for each material composition are essential to achieving good printing quality.

The laser parameter-induced influence on the MMAM-printed microstructure may also be significantly different from the conventional AM technology, such as for the phase transition, the formation of intermetallic compounds and the final mechanical properties. The current commercial 3D design software, phase transition prediction software and simulation & modeling software are usually designed for single-material processing and lack the thermodynamic databases required for multi-materials processing.

All the above issues are the knowledge gaps that need to be filled to push the MMAM technologies from the laboratory investigation to the actual industrial application. Professor Chao Wei explained that “we need to choose the appropriate technology based on the requirement of the final component. Before that, understanding the existing methods is very important for the user to choose the manufacturing method.”

As an emerging field, MMAM has significant advantages in endowing different properties within one component via combining different materials, which is a new degree of freedom to the AM components. Among the potential fields, Professor Wei said that “laser-based MMAM has great potential in the metal functional 3D structures, energy storage components and print tissues and organs in the biomedical fields.”

One of the lead researchers, Professor Lin Li commented that “laser-based MMAM technologies have obvious advantages in simplifying the manufacturing process, increasing design freedom, and reducing the time and costs of prototype manufacturing, compared with conventional manufacturing methods. Our work only opens the door to this new research paradise. We hope that more researchers can enter this field and jointly promote the development of MMAM technologies.”

The future MMAM research is obviously multidisciplinary, involving mechanical engineering, manufacturing engineering, materials science, electronics, photonics, biology and other disciplines. Integrating complex hybrid manufacturing systems, establishing new laws for MMAM designing and manufacturing, high-throughout optimizing processing parameters, artificial intelligence-based quality monitoring and controlling, and assessing the long-term reliability of printed parts need to be further studied. However, we believe that under the guidance of the actual industrial application demand and through the collaborative research of the academic community, these problems will eventually be solved.

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Step Closer Towards Embedding Logic Circuits in Wearables

ELE Times - Fri, 09/02/2022 - 08:38

For all the talk about embedding computers in clothing, here’s an interesting option. Make the clothing the computer, and do it without electricity.

Mechanical engineers at Rice University’s George R. Brown School of Engineering are trying the concept on for size with a set of textile-based pneumatic computers capable of digital logic, onboard memory and user interaction.

The lab’s “fluidic digital logic” takes advantage of how air flows through a series of “kinked” channels to form bits, the 1s, and 0s in computer memories.

The idea is to have such textile-based logic gates support pneumatic actuators, potentially in conjunction with an energy harvesting system developed by the Preston lab, to help people with functional limitations with their day-to-day tasks.

Preston said the lab’s logic-enabled textiles can be mass produced using existing clothes-manufacturing processes and are resilient enough to withstand everyday use. The researchers claimed the embedded gates are both comfortable and tough enough to drive a truck over without damaging them. (And they proved it.)

“The idea of using fluids to construct digital logic circuits is not new,” he said. “And in fact, in the last decade, people have been moving towards implementing fluidic logic in soft materials, things like elastomers. But so far, no one had taken the step to implement it in sheet-based materials, a feat which required redesigning the entire approach from first principles.”

The lab tested its logic on devices that assist users with physical motion and a system to raise and lower a hood with the push of a button, no electricity involved, for thermoregulation.

“We think there’s a host of ways this can be implemented to help people go about their daily activities,” Preston said. “One of the next areas we’re looking into is sensing intent. As soon as the wearer initiates a course of action, we can then offer assistance for the remainder of that action.”

“For example, you might start to grasp an object and if the system senses your intent, it will give you some assistance in closing your hand around that object so you can lift it up,” he said.

At the center of the concept sits a “NOT” gate, a basic component of computer circuitry also known as an inverter. This logic gate’s output is the inverse (or opposite) of the input. In an electronic circuit, the gate is on or off (1 or 0), but the pneumatic gate replaces those terms with “high” or “low” air pressure.

“We think of the logic element as, at its most fundamental level, containing both a relay and a fluidic resistor,” said Anoop Rajappan, a Rice postdoctoral fellow and lead author of the paper. “These would be equivalent to having an electronic relay or transistor paired with the resistor, which is the foundation of typical transistor-resistor logic.”

The pneumatic system depends on a concept Preston describes as a mathematically designed kink geometry, implemented in pressure-controllable valves that cut the flow of air the same way a bent garden hose stops water.

The valves, each about a square inch in size, are laminated into the textiles and have proved robust enough to handle 20,000 on-off cycles and 1 million flex cycles, as well as 20 cycles in a standard household washing machine.

Preston noted the research team includes Stanford University postdoctoral fellow Vanessa Sanchez, a fashion designer-turned-engineer who gained chops with training from the Fashion Institute of Technology in New York City and a subsequent Ph.D. in mechanical engineering and materials science from Harvard University and its Wyss Institute.

Co-authors of the paper are Rice graduate students Barclay Jumet, Zhen Liu and Faye Yap, alumna Rachel Shveda and undergraduate Colter Decker.

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4D vision sensor doubles detection range

EDN Network - Thu, 09/01/2022 - 20:10

SiLC Technologies’ Eyeonic vision sensor can perceive, identify, and avoid objects at a range of more than 1 kilometer. Having previously demonstrated a detection range of 500 meters earlier this year at CES 2022, SiLC has optimized its technology to go beyond 1000 meters.

At the heart of the Eyeonic device is a silicon photonic chip that integrates frequency modulated continuous wave (FMCW) LiDAR. The chip provides all the photonics functions needed to enable a coherent 4D vision sensor in a tiny footprint, while meeting low-cost and low-power demands. The company believes the chip is the only readily integratable solution for manufacturers building autonomous vehicles, industrial robots, and security systems.

“Our technology platform is flexible enough to address ultra-long-range to ultra-short-range applications, which speaks to our understanding of what is needed to truly make machine vision as good or better than human vision,” said Dr. Mehdi Asghari, SiLC’s CEO and founder. “The highly detailed, accurate instantaneous velocity and ultra-long-range information that our Eyeonic Vision Sensor provides is the key to helping robots classify and predict their environment—in the same way that the human eye and brain process together.”

To facilitate customer development efforts, SiLC offers reference designs and a range of key components needed to develop a full solution. Examples of fully configured systems, based on the Eyeonic platform, will be made available as prototypes to enable rapid evaluation by customers and end users.

Eyeonic product page

SiLC Technologies

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High-frequency flex circuit boosts signal integrity

EDN Network - Thu, 09/01/2022 - 20:05

Nortech has patented it Flex Faraday Xtreme (FFX), a flexible printed circuit for transmitting high frequency signals while controlling both crosstalk and impedance. FFX also minimizes electromagnetic interference and improves parallel transmission alignment.

“Nortech’s commitment to innovative technologies is key to supporting our strategic medical, industrial, and defense customers,” said Jay D. Miller, CEO and president. “In highly complex and compact devices, the FFX is designed to provide the signal integrity necessary to support sensitive, mission-critical applications.”

“With our patent of the Flex Faraday Xtreme, Nortech provides intelligent transmission lines that provide benefits over traditional micro coax cables in challenging applications,” said Steve Czeck, senior director of engineering. “FFX technology will be applied to meet customer requirements for size or weight constraints, or where harsh conditions exist.”

FFX is based on the work of Michael Faraday in the 1830s that contributed to the current understanding of shielding effects of what is now called a Faraday cage. U.S. patent number 11,412,608 for FFX is the company’s first patent in its technology portfolio.

FFX adoption will ramp up through 2023. A datasheet was not available at the time of this announcement.

Nortech Systems

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Workflow manager speeds baseband and RFIC designs

EDN Network - Thu, 09/01/2022 - 20:02

A new model generator tool in Keysight’s PathWave Device Modeling 2023 environment improves automation across the entire workflow. The model generator serves as a workflow manager that enables one-click import of measured data, creation of trend plots, organization of extraction flow, basic QA verification, and documentation.

Semiconductor device modeling engineers can use PathWave Device Modeling 2023 to automate the creation of accurate simulation models and process design kits for baseband and RF IC designs that employ both silicon (CMOS) and compound III-V technologies. The integrated circuit characterization and analysis program (IC-CAP) extracts accurate compact models and includes measurement, simulation, optimization, and statistical analysis tools. Workflow and speed improvements help achieve a flexible and open environment that integrates all of Keysight’s modeling products.

Other enhanced products in the 2023 device modeling software suite include PathWave Model Builder, PathWave Model QA, and an advanced low-frequency noise analyzer (A-LFNA). For more information about Keysight PathWave 2023 solutions, visit What’s New in Device Modeling.

PathWave Device Modeling 2023 product page

Keysight Technologies

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SoC touts effortless machine learning

EDN Network - Thu, 09/01/2022 - 19:59

Machine learning company SiMa.ai offers a software-centric MLSoC platform that it says enables effortless ML deployment at the embedded edge. Useful for any computer vision application, the device is manufactured using foundry partner TSMC’s 16-nm power-efficient technology. According to the company, the system-on-chip delivers a 10X improvement in performance/watt compared to alternative solutions, operating at the most efficient frames per second/watt.

The MLSoC features a push-button software experience to easily scale machine learning for robotics, smart vision, autonomous vehicles, drones, healthcare, and use cases in the government sector. The chip’s processing system consists of computer vision processors for image pre- and post-processing, coupled with dedicated machine learning acceleration and high-performance application processors. Surrounding the real-time intelligent video processing are memory interfaces, communication interfaces, and system management all connected via a network on chip.

“We’ve seen over a dozen edge processing solutions, and have never seen anything approaching the performance and power efficiency of SiMa.ai’s MLSoC platform,” said Karl Freund, founder and principal analyst at Cambrian-AI Research. “Their solution is an order of magnitude faster and more energy efficient. So far, they are blowing past their customer’s requirements by accelerating the entire vision processing pipeline, not just the ML inferencing portion. Early customers are finding it extremely easy and simple to implement SiMa.ai into their current solutions.”

In addition to the MLSoC, SiMa.ai offers an evaluation board that mounts the MLSoC on a PCIe card for host computer or stand-alone operation. To learn more, contact SiMa.ai here.

MLSoC product page 


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AMD unveils powerful Zen 4 desktop CPUs

EDN Network - Thu, 09/01/2022 - 19:58

AMD’s Ryzen 7000 series of desktop processors leverages the speed of the Zen 4 microarchitecture to take gaming and content creation to new levels. The initial four Ryzen 7000 CPUs provide up 16 cores, up to 32 threads, as much as 80 MB of cache, and boost clock speeds that top out at 5.7 GHz.

The flagship 16-core Ryzen 9 7950X delivers single-core performance improvements of up to 29% over the previous generation and up to 45% more compute performance for content creation in POV-Ray rendering mode. Further, AMD says the Ryzen 9 7950X offers up to 27% better performance-per-watt. Even the 6-core Ryzen 5 7600X offers an average of 5% faster gaming performance across select titles than the competitor’s flagship gaming processor, according to AMD.

In addition to the 5-nm Zen 4 Ryzen processors, AMD announced Socket AM5 motherboards featuring dual-channel DDR5 memory. With up to 24 PCIe 5.0 lanes, the Socket AM5 is AMD’s most expansive desktop platform to date. Socket AM5 motherboards offer a choice of four new chipsets, giving users the flexibility to choose the exact features they want.

Starting at $299, Ryzen 7000 series desktop processors will be available for purchase on September 27, 2022. Motherboards will be available in September (X670 and X670E chipsets) and October (B650 and B650E chipsets. Motherboard pricing starts at $125.

Ryzen 7000 product page


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Building a personal workstation: CPU selection

EDN Network - Thu, 09/01/2022 - 18:49

In one of last month’s blog posts, I detailed the constituent hardware and software building blocks for a workstation computer I was planning on building…with one key omission. As I mentioned last month, the CPU was a more complicated topic, for which I’d devote a focused post of its own to be published soon. “Soon” is now!

Let’s begin with a few fundamentals. Beginning in late 2008, when AMD (whose original CEO was famous for, among other things, his quote that “Real Men Own Fabs”) divested its fab network into a separate company, GlobalFoundries, AMD has been wholly dependent on foundries to manufacture its microprocessors and companion chipsets, graphics processors, and other products. In recent years, TSMC has been AMD’s primary foundry partner, particularly for its leading-edge semiconductor devices.

However, as you already know from my ongoing Apple coverage, for example, AMD isn’t TSMC’s only partner. That means that AMD’s supply is limited, in contrast to competitor Intel, who’s been struggling in recent years to get its advanced lithography processes into high volume production but still has a captive multi-fab network capable of cranking out very large volumes of product offerings fabricated on less advanced processes. And that means that AMD needs to be selective about what CPU markets it enters in order, to make maximum revenue and profitability return on its comparatively limited foundry supply capacity investments.

The other fundamental I’d like to remind you of is one that AMD largely shares with Intel, and essentially every other processor supplier, for that matter. AMD strives to leverage a common microarchitecture across multiple product families, spanning servers to laptops and embedded systems. AMD also strives to translate each chip design it undertakes into multiple product variants, differing in metrics such as CPU core count (enabling the company to, for example, still sell a die even with one or more nonfunctional cores on it), clock speed, power consumption, cache size, and the like. Where AMD differs from Intel in this latter regard is that its (for the moment, at least) relatively unique “chiplet” strategy also enables the company to mix-and-match multiple die types and counts under a common package lid.

So what CPU am I planning on leveraging for my workstation “build” and how did I pick it? From what I’ve already written here, it’d be a safe bet for you to guess that it’s from AMD! And if you’ve already looked at the motherboard I’ll be using, Gigabyte’s TRX40 DESIGNARE, you know that it’s from AMD’s Threadripper family, specifically the latest third-generation family.

Time for some more background info. AMD’s current microprocessor line spans three high-level product groups: Ryzen for mainstream and gaming mobile and desktop computers (the “Athlon” brand still exists on AMD’s website but the referenced products are quite old, suggesting that it’s been at least temporarily shelved), Threadripper (alternatively known as Ryzen Threadripper) for workstations, and EPYC for servers.

AMD’s current microarchitecture, Zen, is the follow-up to the ultimately underwhelming Bulldozer and its Piledriver, Steamroller and Excavator successors. The original Zen microarchitecture, dating from 2017, has been followed by Zen+, Zen 2 and Zen 3 descendants, all of which make average IPC (instructions per clock) and other enhancements over their forebears.  AMD’s current product offerings, as I mentioned in a writeup last year, are based on a mix of Zen 2 and Zen 3 microarchitectures. That said, Zen 4 has already been announced and may be available in product form (the upcoming Ryzen 7000 series) by the time you read this.

Now let’s translate these big-picture trends into product specifics. Current Ryzen CPUs top out at 16 physical cores (with each core supporting the processing of up to two simultaneous threads), two memory channels absent EDAC (error detection and correction, i.e., ECC, error correction code) support, and up to 24 PCIe lanes. Threadripper extends the multi-die chiplet concept in supporting up to 64 cores (128 threads); commensurate with expanded processing potential, the CPU-plus-chipset supports up to four memory channels (again absent EDAC support) and 64 PCIe lanes. Then there’s Threadripper Pro, which again doubles the maximum memory channels to eight (and reflective of the high-end workstation focus, EDAC-capable memory usage is now also optional) and adds even more PCIe lanes. And finally, there’s EPYC, a “kissing cousin” (as the saying goes) of Threadripper Pro, differing predominantly in support for multiple packaged CPUs per system (reflective of its server focus).

First-generation 1000-series Threadripper and Threadripper Pro products were based on AMD’s original Zen microarchitecture, with 2000-series successors leveraging Zen+. Zen 2-based Threadripper (and Pro) 3000-series offerings came out in late 2019, notably a few months after their EPYC counterparts. The schedule has slipped even further for current generation offerings: Zen 3-based Threadripper Pro 5000 series chips finally emerged earlier this year, marking a 1.5 year delay from when the first Zen 3-based Ryzens were announced, as well as being a year later than EPYC equivalents. But no Zen 3-based standard Threadrippers have appeared, at least yet. Will they ever? Contradictory company officials’ comments make the answer unclear.

This situation is at the root of the “roll the dice” comment I made in last month’s piece:

When I stumbled across an open box but brand new v1.1 Gigabyte TRX40 DESIGNARE on eBay for below MSRP last August, I decided to “roll the dice” (for reasons I’ll explain in a subsequent writeup) and take the plunge.

For its third generation (Zen 2-based) Threadripper offerings, AMD switched to new sockets: sTRX4 (found on my motherboard and corresponding to the TRX40 chipset) for Threadripper 3000-series CPUs, and sWRX8 (related to the WRX80 chipset) for Threadripper Pro 3000-series CPUs. Newer Threadripper Pro 5000 series CPUs also use the sWRX8 socket, providing a potential upgrade path for existing motherboard owners, assuming their manufacturers release requisite BIOS updates. But unless AMD comes out with 5000-series standard Threadrippers, too, the motherboard I bought will end up being a one-generation orphan.

Another business decision that AMD made has further complicated the Threadripper availability (therefore pricing) situation. Initially in full, and still largely the case today, the company directed its processor supply to workstation system manufacturer partners such as Lenovo, bypassing the retail channel in the process. The result: peruse the websites of well-known retailers such as Adorama, Amazon, B&H Photo Video, Best Buy and Newegg and you likely won’t find any Threadripper CPUs available for direct sale (believe me, I know; I’ve been on their waiting lists forever). If you stumble across one, it’ll likely come from a relatively unknown reseller, leveraging the larger retailer as a marketing, and sometimes also warehousing and shipping, partner. The price, reliability, warranty, and other downside implications of such a dubious consumer purchase plunge are likely obvious.

Here are the listed MSRPs for the Threadripper 3000-series family members at introduction:

  • 3960X (24-core): $1399
  • 3970X (32-core): $1999
  • 3990x (64-core): $3990

Under normal supply-vs-demand dynamics, especially this long after introduction, you’d by now typically see retail prices notably lower than MSRP. But between lingering pandemic-induced supply constraints and the absence of a successor product line (whose presence would tend to push down prices on previous-generation offerings), these aren’t normal times. While a retail packaged Threadripper 3960X can typically be found for less than $2,000, the 3970X will set you back $3,500 or so. And the 3990X? If you’ve got $7,829 (each) burning a hole in your pocket, Amazon’s got two of them for sale as I type these words. Contrast those prices both with their past-history MSRP equivalents and with the $449 that a 16-core Zen 3-based Ryzen 9 5950X is currently selling for at Amazon, and the ROI of the substantial incremental Threadripper investment becomes even more unclear (except in specific scenarios demanding the additional core counts and/or aggregate memory bandwidth, of course).

The other (admittedly equally-dubious) sourcing option, which I ended up going with after waiting sorta-patiently but ultimately fruitlessly for a reasonably priced Threadripper 3000-series processor (or, for that matter, a 5000-series successor) to show up at a “known” retailer, was to purchase a “tray” CPU. These are typically overstocks (or illegal inventory redirects?) from systems manufacturers’ warehouses, often listed for sale on eBay and the like. Given AMD’s longstanding partnership with Lenovo, the Threadrippers unsurprisingly are typically sourced from eBay sellers in China. I went with a 24-core Threadripper 3960X, not only the lowest priced product family option in an absolute sense but also the most cost-effective from a price-per-core metric standpoint.

It set me back $1,168 (!!!), and here’s what it looked like when it arrived:

When I bought it, it was listed as “open box”, which makes sense given that it’s a tray unit. But look closely at the package top and you’ll see remnants of thermal paste from presumed prior installation. And in fact, the seller updated the eBay listing post-my-purchase (he had multiple units available for sale) to indicate that the CPUs were in fact “used”, supposedly tested before resale, system “pulls”. One other notable downside of a tray unit purchase is that the cooler mounting bracket, installation tools, etc. normally bundled with a full retail unit aren’t included and need to be separately acquired. eBay fortunately came to the rescue once again:

Even though the CPU wasn’t as-advertised, I didn’t have any other reasonably priced option (putting aside whether $1,000+ is even reasonable), and I was covered by eBay’s 30-day Buyer Protection policy in spite of the seller claiming that he didn’t accept returns. So, I decided to plunge forward into the system build. How did it work out? You’ll need to wait for the next post in the series for the details. Until then, I as-always welcome your thoughts in the comments!

Brian Dipert is Editor-in-Chief of the Edge AI and Vision Alliance, and a Senior Analyst at BDTI and Editor-in-Chief of InsideDSP, the company’s online newsletter.

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LED string partially out.

Reddit:Electronics - Wed, 08/31/2022 - 21:39
LED string partially out.

X-ray revealed an incomplete trace and partiality missing pad.

submitted by /u/Stabutron
[link] [comments]

Lead-free Solar Material with a Built-in Switch

ELE Times - Wed, 08/31/2022 - 15:31

Solar panels, also known as photovoltaics, rely on semiconductor devices, or solar cells, to convert energy from the sun into electricity.

To generate electricity, solar cells need an electric field to separate positive charges from negative charges. To get into this field, manufacturers typically dope the solar cell with chemicals so that one layer of the device bears a positive charge and another layer has a negative charge. This multi-layered design ensures that electrons flow from the negative side of a device to the positive side—a key factor in device stability and performance. But chemical doping and layered synthesis also add extra costly steps in solar cell manufacturing.

Now, a team of researchers led by scientists at DOE’s Lawrence Berkeley National Laboratory (Berkeley Lab), in collaboration with UC Berkeley, have demonstrated a unique workaround that offers a simpler approach to solar cell manufacturing: A crystalline solar material with a built-in electric field—a property enabled by what scientists call “ferroelectricity.

The new ferroelectric material—which is grown in the lab from cesium germanium tribromide (CsGeBr3 or CGB)—opens the door to an easier approach to making solar cell devices. Unlike conventional solar materials, CGB crystals are inherently polarized, where one side of the crystal builds up positive charges and the other side builds up negative charges, with no doping required.

In addition to being ferroelectric, CGB is also a lead-free “halide perovskite,” an emerging class of solar materials that have intrigued researchers for their affordability and ease of synthesis compared to silicon. But many of the best-performing halide perovskites naturally contain the element lead. According to other researchers lead remnants from perovskite solar material production and disposal could contaminate the environment and present public health concerns. For these reasons, researchers have sought new halide perovskite formulations that eschew lead without compromising performance.

“If you can imagine a lead-free solar material that not only harvests energy from the sun but also has the added bonus of having a natural, spontaneously formed electric field—the possibilities across the solar energy and electronics industries are pretty exciting,” said co-senior author Peidong Yang, a leading nanomaterials expert known for his pioneering work in one-dimensional semiconducting nanowires for novel solar cell technologies and artificial photosynthesis. He is a senior faculty scientist in Berkeley Lab’s Materials Sciences Division and a professor of chemistry and materials science and engineering at UC Berkeley.

CGB could also advance a new generation of switching devices, sensors, and super-stable memories that respond to light said co-senior author Ramamoorthy Ramesh, who held titles of senior faculty scientist in Berkeley Lab’s Materials Sciences Division and professor of materials science and engineering at UC Berkeley at the time of the study and is now vice president of research at Rice University.

Perovskite solar films are typically made using low-cost solution-coating methods, such as spin coating or inkjet printing. And unlike silicon, which requires a processing temperature of about 2,732 degrees Fahrenheit to manufacture into a solar device, perovskites are easily processed from solution at room temperature to around 300 degrees Fahrenheit—and for manufacturers, these lower processing temperatures would dramatically reduce energy costs.

But despite their potential boost to the solar energy sector, perovskite solar materials won’t be market-ready until researchers overcome long-standing challenges in product synthesis and stability and material sustainability.

Pinning down the perfect ferroelectric perovskite

Perovskites crystallize from three different elements, and each perovskite crystal is delineated by the chemical formula ABX3

Most perovskite solar materials are not ferroelectric because their crystalline atomic structure is symmetrical, like a snowflake. In the past couple of decades, renewable energy researchers like Ramesh and Yang have been on the hunt for exotic perovskites with ferroelectric potential—specifically, asymmetrical perovskites.

A few years ago, first author Ye Zhang, who was a UC Berkeley graduate student researcher in Yang’s lab at the time, wondered how she could make a lead-free ferroelectric perovskite. She theorized that placing a germanium atom in the center of a perovskite would distort its crystallinity just enough to engender ferroelectricity. On top of that, a germanium-based perovskite would free the material of lead. (Zhang is now a postdoctoral researcher at Northwestern University.)

But even though Zhang had honed in on germanium, there were still uncertainties. After all, conjuring up the best lead-free, ferroelectric perovskite formula is like finding a needle in a haystack. There are thousands of possible formulations.

So Yang, Zhang, and the team partnered with Sinéad Griffin, a staff scientist in Berkeley Lab’s Molecular Foundry and Materials Sciences Division who specializes in the design of new materials for a variety of applications, including quantum computing and microelectronics.

With support from the Materials Project, Griffin used supercomputers at the National Energy Research Scientific Computing Center (NERSC) to perform advanced theoretical calculations based on a method known as density-functional theory.

Through these calculations, which take atomic structure and chemical species as input and can predict properties such as the electronic structure and ferroelectricity, Griffin and her team zeroed in on CGB, the only all-inorganic perovskite that checked off all the boxes on the researchers’ ferroelectric perovskite wish list: Is it asymmetrical? Yes, its atomic structure looks like a rhombohedran, rectangle’s crooked cousin. Is it really a perovskite? Yes, its chemical formula—CeGeBr3 –matches the perovskite’s telltale structure of ABX3.

The researchers theorized that the asymmetric placement of germanium in the center of the crystal would create a potential that, like an electric field, separates positive electrons from negative electrons to produce electricity. But were they right?

Measuring CGB’s ferroelectric potential

To find out, Zhang grew tiny nanowires (100 to 1,000 nanometers in diameter) and nanoplates (around 200 to 600 nanometers thick and 10 microns wide) of single-crystalline CGB with exceptional control and precision.

“My lab has been trying to figure out how to replace lead with less toxic materials for many years,” said Yang. “Ye developed an amazing technique to grow single-crystal germanium halide perovskites —and it’s a beautiful platform for studying ferroelectricity.”

X-ray experiments at the Advanced Light Source revealed CGB’s asymmetrical crystalline structure, a signal of ferroelectricity. Electron microscopy experiments led by Xiaoqing Pan at UC Irvine uncovered more evidence of CGB’s ferroelectricity: a “displaced” atomic structure offset by the germanium center.

Meanwhile, electrical measurement experiments carried out in the Ramesh lab by Zhang and Eric Parsonnet, a UC Berkeley physics graduate student researcher and co-author on the study, revealed a switchable polarity in CGB, satisfying yet another requirement for ferroelectricity.

But a final experiment—photoconductivity measurements in Yang’s UC Berkeley lab—yielded a delightful result and a surprise. The researchers found that CGB’s light absorption is tunable—spanning the spectrum of visible to ultraviolet light (1.6 to 3 electron volts), an ideal range for coaxing high energy conversion efficiencies in a solar cell, Yang said. Such tunability is rarely found in traditional ferroelectrics, he noted.

Yang says there is still more work to be done before the CGB material can make its debut in a commercial solar device, but he’s excited by their results so far. “This ferroelectric perovskite material, which is essentially a salt, is surprisingly versatile,” he said. “We look forward to testing its true potential in a real photovoltaic device.”

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SiMa.ai Ships First Industry Leading Purpose-built Machine Learning SoC

ELE Times - Wed, 08/31/2022 - 15:14

SiMa.ai today announced that it has begun shipping the industry’s first purpose-built software-centric Machine Learning System-on-Chip platform for the embedded edge – the MLSoC.

The $1 trillion global embedded edge market is currently reliant on legacy technology that limits the pace of innovation. Today’s computer vision applications utilizing ML require far too much power, are difficult to deploy and lack scalability. SiMa.ai addresses these shortcomings with its disruptive purpose-built platform which enables Effortless ML deployment and scaling at the edge. Any computer vision application is now possible with push-button ML capability, enabling rapid design iterations in minutes, all at 10x better performance per watt. The SiMa.ai MLSoC Platform offers an unmatched user experience for customers looking to scale and future-proof without the steep learning curve.

“When we started SiMa.ai 3.5 years ago, we set out to deliver a disruptive 10x performance improvement over alternatives and provide a scalable industry-leading ML experience solving computer vision applications,” said Krishna Rangasayee, CEO and Founder, SiMa.ai. “Today we are delighting customers by delivering on that promise and exceeding their expectations. We are excited to take our very first purpose-built software-centric MLSoC to volume production. This first-time-right success was made possible by a great team, fantastic technology partnerships, and our investors. I would like to thank them all for believing in our mission.”

“We’ve seen over a dozen edge processing solutions, and have never seen anything approaching the performance and power efficiency of SiMa.ai’s MLSoC platform,” said Karl Freund, Founder and Principal Analyst at Cambrian-AI Research. “Their solution is an order of magnitude faster and more energy efficient. So far, they are blowing past their customer’s requirements by accelerating the entire vision processing pipeline, not just the ML inferencing portion. Early customers are finding it extremely easy and simple to implement SiMa.ai into their current solutions.”

To provide the highest quality in ML innovation, SiMa.ai has chosen key partners with an industry-leading track record. Today the company has announced partnerships with TSMC, Synopsys, Arm, Allegro, GUC and Arteris. These companies provide first-class technology to align with SiMa.ai’s design methodology.

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Optimization Fluid Mixing with Machine Learning

ELE Times - Wed, 08/31/2022 - 15:04

Fluid mixing is an important part of several industrial processes and chemical reactions. However, the process often relies on trial-and-error-based experiments instead of mathematical optimization. While turbulent mixing is effective, it cannot always be sustained and can damage the materials involved. To address this issue, researchers from Japan have now proposed an optimization approach to fluid mixing for laminar flows using machine learning, which can be extended to turbulent mixing as well.

The mixing of fluids is a critical component in many industrial and chemical processes. Pharmaceutical mixing and chemical reactions, for instance, may require homogeneous fluid mixing. Achieving this mixing faster and with less energy would reduce the associated costs greatly. In reality, however, most mixing processes are not mathematically optimized and instead rely on trial-and-error-based empirical methods. Turbulent mixing, which uses turbulence to mix up fluids, is an option but is problematic as it is either difficult to sustain (such as in micro-mixers) or damages the materials being mixed (such as in bioreactors and food mixers).

Can an optimized mixing be achieved for laminar flows instead? To answer this question, a team of researchers from Japan, in a new study, turned to machine learning. The team resorted to an approach called “reinforcement learning” (RL), in which intelligent agents take actions in an environment to maximize the cumulative reward (as opposed to an instantaneous reward).

“Since RL maximizes the cumulative reward, which is global-in-time, it can be expected to be suitable for tackling the problem of efficient fluid mixing, which is also a global-in-time optimization problem,” explains Associate Professor Masanobu Inubushi, the corresponding author of the study. “Personally, I have a conviction that it is important to find the right algorithm for the right problem rather than blindly apply a machine learning algorithm. Luckily, in this study, we managed to connect the two fields (fluid mixing and reinforcement learning) after considering their physical and mathematical characteristics.” The work included contributions from Mikito Konishi, a graduate student, and Prof. Susumu Goto, both from Osaka University.

One major roadblock awaited the team, however. While RL is suitable for global optimization problems, it is not particularly well-suited for systems involving high-dimensional state spaces, i.e., systems requiring a large number of variables for their description. Unfortunately, fluid mixing was just such a system.

To address this issue, the team adopted an approach used in the formulation of another optimization problem, which enabled them to reduce the state space dimension for fluid flow to one. Put simply, the fluid motion could now be described using only a single parameter.

The RL algorithm is usually formulated in terms of a Markov decision process (MDP), a mathematical framework for decision making in situations where the outcomes are part random and partly controlled by the decision maker. Using this approach, the team showed that RL was effective in optimizing fluid mixing.

“We tested our RL-based algorithm for the two-dimensional fluid mixing problem and found that the algorithm identified an effective flow control, which culminated in an exponentially fast mixing without any prior knowledge,” says Dr. Inubushi. “The mechanism underlying this efficient mixing was explained by looking at the flow around the fixed points from a dynamical system theory perspective.”

Another significant advantage of the RL method was an effective transfer learning (applying the knowledge gained to a different but related problem) of the trained mixer. In the context of fluid mixing, this implied that a mixer trained at a certain Péclet number (the ratio of the rate of advection to the rate of diffusion in the mixing process) could be used to solve a mixing problem at another Péclet number. This greatly reduced the time and cost of training the RL algorithm.

While these results are encouraging, Dr. Inubishi points out that this is still the first step. “There are still many issues to be solved, such as the method’s application to more realistic fluid mixing problems and improvement of RL algorithms and their implementation methods,” he says.

While it is certainly true that two-dimensional fluid mixing is not representative of the actual mixing problems in the real world, this study provides a useful starting point. Moreover, while it focuses on mixing in laminar flows, the method is extendable to turbulent mixing as well. It is, therefore, versatile and has the potential for major applications across various industries employing fluid mixing.

The post Optimization Fluid Mixing with Machine Learning appeared first on ELE Times.

Nissan joins Siemens to digitalize EV powertrain production

EDN Network - Tue, 08/30/2022 - 16:04

For its new all-electric crossover Ariya, Nissan is digitalizing its EV production line in Tochigi, Japan while employing the Internet of Things (IoT)-enabled hardware, software, and digital services from Siemens. That will allow the Japanese automaker to standardize the processing and assembly of the electric powertrain at its smart factory in Tochigi.

The software deployment includes Siemens’ safety PLC Simatic S7-1500, ET200SP distributed I/ O module as well as Profinet, which creates end-to-end communication from the field to the management level. Then there is TIA Portal, an engineering framework fully integrated into all automation devices.

Source: Siemens Digital Industries Software

Nissan is also implementing Siemens’ diagnostic commissioning system Sidis Pro for data writing into electronic control units (ECUs) and verifying automotive electric components. Sidis Pro, a vehicle diagnosis and inspection data management system, provides optimal support for inspection processes in highly-automated automobile production systems. It can also be used for other applications to enable system standardization and optimize resources at vehicle production lines.

Such end-to-end digital threads allow automakers like Nissan to connect various information sources across product lifecycle management (PLM) platforms. Moreover, they enable access to the entire digitalized automation process: from digital planning to integrated engineering and transparent operation.

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Demonstration of Biodegradable Printed Circuit

ELE Times - Tue, 08/30/2022 - 15:25

According to the United Nations, less than a quarter of all U.S. electronic waste gets recycled. In 2021 alone, global e-waste surged to 57.5 million tons, and only 17.4% of that was recycled.

Some experts predict that our e-waste problem will only get worse over time because most electronics on the market today are designed for portability, not recyclability. Tablets and readers, for example, are assembled by gluing circuits, chips, and hard drives to thin layers of plastic, which must be melted to extract precious metals like copper and gold. Burning plastic releases toxic gases into the atmosphere, and electronics waste away in landfill often contain harmful materials like mercury, lead, and beryllium.

But now, a team of researchers from the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley has developed a potential solution: a fully recyclable and biodegradable printed circuit. The researchers say that the advance could divert wearable devices and other flexible electronics from landfill, and mitigate the health and environmental hazards posed by heavy metal waste.

“When it comes to plastic e-waste, it’s easy to say it’s impossible to solve and walk away,” said senior author Ting Xu, a faculty senior scientist in Berkeley Lab’s Materials Sciences Division, and professor of chemistry and materials science and engineering at UC Berkeley. “But scientists are finding more evidence of significant health and environmental concerns caused by e-waste leaching into the soil and groundwater. With this study, we’re showing that even though you can’t solve the whole problem yet, you can at least tackle the problem of recovering heavy metals without polluting the environment.”

Putting enzymes to work

In a previous Nature study, Xu and her team demonstrated a biodegradable plastic material embedded with purified enzymes such as Burkholderia cepacian lipase (BC-lipase). Through that work, they discovered that hot water activates BC-lipase, prompting the enzyme to degrade polymer chains into monomer building blocks. They also learned that BC-lipase is a finicky “eater.” Before a lipase can convert a polymer chain into monomers, it must first catch the end of a polymer chain. By controlling when the lipase finds the chain end, it is possible to ensure the materials don’t degrade until the water reaches a certain temperature.

For the current study, Xu and her team simplified the process even further. Instead of expensive purified enzymes, the biodegradable printed circuits rely on cheaper, shelf-ready BC lipase “cocktails.” This significantly reduces costs, facilitating the printed circuit’s entry into mass manufacturing, Xu said.

By doing so, the researchers advanced the technology, enabling them to develop a printable “conductive ink” composed of biodegradable polyester binders, conductive fillers such as silver flakes or carbon black, and commercially available enzyme cocktails. The ink gets its electrical conductivity from the silver or carbon black particles, and the biodegradable polyester binders act as glue.

The researchers supplied a commercial 3D printer with conductive ink to print circuit patterns onto various surfaces such as hard biodegradable plastic, flexible biodegradable plastic, and cloth. This proved that the ink adheres to a variety of materials, and forms an integrated device once the ink dries.

To test its shelf life and durability, the researchers stored a printed circuit in a laboratory drawer without controlled humidity or temperature for seven months. After pulling the circuit from storage, the researchers applied continuous electrical voltage to the device for a month and found that the circuit conducted electricity just as well as it did before storage.

Next, the researchers put the device’s recyclability to test by immersing it in warm water. Within 72 hours, the circuit materials degraded into their constituent parts—the silver particles completely separated from the polymer binders, and the polymers broke down into reusable monomers, allowing the researchers to easily recover the metals without additional processing. By the end of this experiment, they determined that approximately 94% of the silver particles can be recycled and reused with similar device performance.

That the circuit’s degradability continued after 30 days of operation surprised the researchers, suggesting that the enzymes were still active. “We were surprised that the enzymes ‘lived’ for so long. Enzymes aren’t designed to work in an electric field,” Xu said.

Xu attributes the working enzymes’ longevity to the biodegradable plastic’s molecular structure. In their previous study, the researchers learned that adding an enzyme protectant called random heteropolymer, or RHP, helps to disperse the enzymes within the mixture in clusters a few nanometers (billionths of a meter) in size. This creates a safe place in the plastic for enzymes to lie dormant until they’re called to action.

The circuit also shows promise as a sustainable alternative to single-use plastics used in transient electronics—devices such as biomedical implants or environmental sensors that disintegrate over a period of time, said researcher Junpyo Kwon, a Ph.D. student researcher from the Xu Group at UC Berkeley.

Now that they’ve demonstrated a biodegradable and recyclable printed circuit, Xu wants to demonstrate a printable, recyclable, and biodegradable microchip.

“Given how sophisticated chips are nowadays, this certainly won’t be easy. But we have to try and give our level best,” she said.

The post Demonstration of Biodegradable Printed Circuit appeared first on ELE Times.


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